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Training about Research Data Management - introduction You will learn: History about 3TU Datacentre Incorporate data management planning in your research workflow. The key importance of persistent identifiers and data citation in the global scholarly information infrastructure. The importance of proper data archiving.
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Research Data Management Workshop101 for PhD students, starting their academic career
[2014] CC-BY: 3TU.Datacentre
more info @ www.datacentrum.3tu.nl/en/
What will you learn today
• History about 3TU Datacentre
• Incorporate data management planning in your research workflow
• The key importance of persistent identifiers and data citation in the global scholarly information infrastructure.
• The importance of proper data archiving.
Data Wave
Source: http://www.odgersberndtson.com/en/observe/article/riding-the-data-tidal-wave-7659/
HISTORY
Sources:http://research.microsoft.com/en-us/collaboration/fourthparadigm/http://cordis.europa.eu/fp7/ict/e-infrastructure/docs/hlg-sdi-report.pdf http://www.knowledge-exchange.info/Default.aspx?ID=469
Introduction 3TU.Datacenter• Federation of 3 Technical Universities
– (Delft, Twente, Eindhoven)– Mandate for Research Data Management
• 3TU.Datacenter: start 2008– 12 employees– Archive: 90 TB storage (incl. mirror+backup),
6500 Datasets, ~350 up- & downloads /year,
– Labs: Open Earth, Dataverse, Share
– Services: Datamanagement planning Advise,
DOImining/DataCite, Training, etc.
€
Data archiving: After Research project
Data capture: During Research project
Data Archive
Data planning: Before Research project DMP
Data Lab
Services overview
Research Data
Create
Describe &
Store
Identify &
Register
Discover &
Access
Exploit
Data captureEarly meta data
Data model design
Complete meta dataData repositoryCreate ‘resource maps’/ ‘scientific publication packages’
Assign DOIs‘Publish’ meta dataLinked data
BrowseSearch
Query onlineGoogle maps/earth
Support data-labsData mining support
[ANDS Verbs]
Archives, Labs & Services
Goal: long-term access to research data
• Data archive– Main goal: ‘freeze’ a dataset (version) for future use.
‘Published’ data, simple or complex, varying meta-data, …• Data lab
– Main goal: exchange of data and other research material.Collaboration platforms, specific to discipline/content. Differing in access policies, functionality, size, …
• Data services– Main goal: stimulate improvements in data management.
Advice on standards, licensing & training, support in data documentation, tool development, search and retrieval, …
PREVIEW OF TODAY
Data Management Planning
Persistent Identifiers: Data Claiming and Citation
Data Archiving:Trust and Quality Assurance
Data Archive
INTRODUCTION ROUNDEnjoy today!